Evidence map›Paper›PMID 24991935›Full record

ArticlePloS one2014

Novel bioinformatics method for identification of genome-wide non-canonical spliced regions using RNA-Seq data.

Yongsheng Bai, Justin Hassler, Ahdad Ziyar, Philip Li, Zachary Wright, Rajasree Menon, Gilbert S Omenn, James D Cavalcoli, Randal J Kaufman, Maureen A Sartor

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.2field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 12 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 1 country.

Yongsheng BaiDepartment of Computational Medicine and Bioinformatics, and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America; Morgridge Institute for Research, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
Justin HasslerSanford-Burnham Medical Research Institute, La Jolla, California, United States of America.
Ahdad ZiyarSanford-Burnham Medical Research Institute, La Jolla, California, United States of America.
Philip LiSanford-Burnham Medical Research Institute, La Jolla, California, United States of America.
Zachary WrightDepartment of Computational Medicine and Bioinformatics, and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America.
Rajasree MenonDepartment of Computational Medicine and Bioinformatics, and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America.
Gilbert S OmennDepartment of Computational Medicine and Bioinformatics, and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America; Departments of Internal Medicine and Human Genetics, and School of Public Health, University of Michigan, United States of America; Institute for Systems Biology, Seattle, Washington, United States of America.
James D CavalcoliDepartment of Computational Medicine and Bioinformatics, and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America.
Randal J KaufmanSanford-Burnham Medical Research Institute, La Jolla, California, United States of America.
Maureen A SartorDepartment of Computational Medicine and Bioinformatics, and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America; Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America.
Sanford Burnham Prebys Medical Discovery Institute · USUniversity of Michigan–Ann Arbor · USInstitute for Systems Biology · USMichigan United · USUniversity of Wisconsin–Madison · US

Funding

Tumor Microenvironment and Cancer ImmunologyP30CA030199 · NCI · SANFORD BURNHAM PREBYS MEDICAL DISCOVERY INSTITUTE · PI Michael Jackson · 1985 to 2026
$107.2M
Regulation of Factor VIII SecretionR01HL052173 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KAUFMAN, RANDAL J. · 1995 to 2014
$6.5M
Role of the Unfolded Protein Response in Beta CellR37DK042394 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KAUFMAN, RANDAL J. · 2007 to 2016
$4.9M
Regulation of ER stress-induced cell deathR01DK088227 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KAUFMAN, RANDAL J. · 2010 to 2014
$3.1M
Unfolded Protein Response in Glucose HomeostasisR01DK042394 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KAUFMAN, RANDAL J. · 2003 to 2006
$1.2M
NCI NIH HHS P30 CA030199NHLBI NIH HHS HL052173NHLBI NIH HHS R01 HL052173NIDDK NIH HHS DK042394NIDDK NIH HHS DK088227NIDDK NIH HHS R01 DK042394NIDDK NIH HHS R01 DK088227NIDDK NIH HHS R37 DK042394
6 · The paper itself

Abstract

settingDuring endoplasmic reticulum (ER) stress, the endoribonuclease (RNase) Ire1α initiates removal of a 26 nt region from the mRNA encoding the transcription factor Xbp1 via an unconventional mechanism (atypically within the cytosol). This causes an open reading frame-shift that leads to altered transcriptional regulation of numerous downstream genes in response to ER stress as part of the unfolded protein response (UPR). Strikingly, other examples of targeted, unconventional splicing of short mRNA regions have yet to be reported.

objectiveOur goal was to develop an approach to identify non-canonical, possibly very short, splicing regions using RNA-Seq data and apply it to ER stress-induced Ire1α heterozygous and knockout mouse embryonic fibroblast (MEF) cell lines to identify additional Ire1α targets.

resultsWe developed a bioinformatics approach called the Read-Split-Walk (RSW) pipeline, and evaluated it using two Ire1α heterozygous and two Ire1α-null samples. The 26 nt non-canonical splice site in Xbp1 was detected as the top hit by our RSW pipeline in heterozygous samples but not in the negative control Ire1α knockout samples. We compared the Xbp1 results from our approach with results using the alignment program BWA, Bowtie2, STAR, Exonerate and the Unix "grep" command. We then applied our RSW pipeline to RNA-Seq data from the SKBR3 human breast cancer cell line. RSW reported a large number of non-canonical spliced regions for 108 genes in chromosome 17, which were identified by an independent study.

conclusionsWe conclude that our RSW pipeline is a practical approach for identifying non-canonical splice junction sites on a genome-wide level. We demonstrate that our pipeline can detect novel splice sites in RNA-Seq data generated under similar conditions for multiple species, in our case mouse and human.

Indexed as

RNA SplicingAnimalsBase SequenceCell LineCell Line, TumorDNA-Binding ProteinsEndoplasmic Reticulum StressEndoribonucleasesGenomicsHeterozygoteHumansIntronsMiceMice, KnockoutMolecular Sequence DataProtein Serine-Threonine KinasesDNA-Binding ProteinsEndoribonucleasesErn1 protein, mouseProtein Serine-Threonine KinasesRegulatory Factor X Transcription FactorsTranscription FactorsX-Box Binding Protein 1XBP1 protein, humanXbp1 protein, mouse

Identifiers

PMID24991935
PMCPMC4084626
OpenAlexW2051797957

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.